Machine Learning in CSC 196K What motivated machine learning? What is machine Learning? Statistics and ML algorithms design

Machine learning is motivated by a class of problems No traditional algorithmic solution Computationally expensive Traditional algorithm Input => algorithm => Output When algorithm=?, we may use machine learning algorithm to construct a mapping function between input and output.

Input and Output for Machine Learning Algorithm For the class of ML algorithms that generalization performance is based on a given finite number of training examples, we have: Machine Learning algorithm Mapping function Training set

Statistics and ML algorithms design ML methods overcome shortcomings of statistical methods (estimation, classification, prediction) Learning bias – prior assumption of a Bayesian Nonlinear problems -- regression, linear model Learning systems that use a hypothesis space -- hypothesis testing Statistical learning theory (SVM -Support Vector Machine)

Types of Data mining Techniques in CSC 196K Techniques for data mining covered in csc196K Data warehousing – OLAP and data preprocessing Statistics Machine learning This course only covers data mining related ML techniques and concepts (a subset of ML methods) Data mining is one of many applications of machine learning

To Learn more on Machine Learning and Applications CSC 219 Machine Learning CSC 215 Artificial Intelligence CSC 196L Intelligent Systems Publications in CSUS Library On-line info